3D bioprinting builds tissue-engineered constructs by depositing bioinks in precise geometries. However, balancing bioink formulation, extrusion settings, and cell viability is challenging: slight shifts in one parameter (e.g., viscosity, pressure, temperature) can drastically affect outcomes. Traditional trial-and-error approaches are laborious and low-throughput, often requiring dozens of manual experiments to identify workable settings for a single bioink–cell combination.

Machine learning offers a powerful solution by learning from a limited set of well-controlled experiments to predict outcomes across untested parameter combinations. This dashboard presents a supervised-learning framework for bioprinting optimization using a Random Forest classifier to predict cell viability categories.